Advanced particle swarm assisted genetic algorithm for constrained optimization problems

Dhadwal, Manoj Kumar; Jung, Sung Nam; Kim, Chang Joo

doi:10.1007/s10589-014-9637-0

Cited by 55 publications

(21 citation statements)

References 33 publications

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“…The position update is positioned through some hybrid mechanisms of the GA. Therefore, according to these tips, these two algorithms can be complementary to one another [18], so that the hybrid method, due to its high sensitivity in the selection of optimal point, presents a good solution for multiobjective functions. The HGAPSO algorithm consists of four major operators: enhancement, selection, crossover, and mutation.…”

Section: Optimization Methodsmentioning

confidence: 99%

“…In conjunction with the four main steps in the HGAPSO algorithm, it can be explained in brief as follows [18]: Enhancement: In this section, consideration was made for the elites in a population.…”

Section: Optimization Methodsmentioning

confidence: 99%

“…This operation mimics the maturing phenomenon after knowing the society. Enhancement of the elites was performed by velocity and position update procedures in PSO equations that are as follows [18]: …”

Section: Optimization Methodsmentioning

confidence: 99%

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Optimization of an experimental unsymmetrical-unbalanced two-phase induction motor using the HGAPSO hybrid technique and the finite element method for increasing efficiency and reducing torque ripple

Horiyat¹,

Shariatmadar²,

Vahid³

2016

Turk J Elec Eng & Comp Sci

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Abstract:Two-phase induction motors make an important contribution to social welfare. Accordingly, enhanced quality in such motors would benefit society. This study was done to test this type of motor to determine a nonlinear optimal point in the form of a multiobjective function between ripple of the torque and motor efficiency with consideration of a weighted coefficient in an unbalanced-unsymmetrical two-phase induction motor. Tests demonstrated that minimizing total harmonic distortion could reduce torque ripple. The optimal point was determined using optimization algorithms, and a hybrid of genetic and particle swarm optimization algorithms simultaneously in order to increase efficiency and to

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Section: Optimization Methodsmentioning

confidence: 99%

Section: Optimization Methodsmentioning

confidence: 99%

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Optimization of an experimental unsymmetrical-unbalanced two-phase induction motor using the HGAPSO hybrid technique and the finite element method for increasing efficiency and reducing torque ripple

Horiyat¹,

Shariatmadar²,

Vahid³

2016

Turk J Elec Eng & Comp Sci

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show abstract

“…Energy-efficient routing protocols like topology-based WBAN [26], multi-hop based WBAN [27], medium access control based WBAN [28,29], and priority-based WBAN [30] are proposed by the authors. In order to reduce the consumption of energy in an efficient manner, various optimization algorithms [31][32][33][34][35][36] have been explored in the area of wireless technology [37][38][39][40][41][42][43][44][45]. However, such algorithms don't focus much on WBAN.…”

Section: Related Workmentioning

confidence: 99%

PSOBAN: a novel particle swarm optimization based protocol for wireless body area networks

2019

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In the modern world, wireless body area networks (WBAN) is projected to play a vital role in biomedical and psychological applications. The practical implementation of WBAN technology surfer from various deployment issues that have to be dealt with. The more serious concern is associated with the energy consumption of these networks. Biosensor nodes continuously sense the signals and send the same to sink. Sending data to sink is an energy-consuming operation, so routing is done to optimize energy utilization in WBAN. The continuous data sensing and the transmission of information over long-distances result in huge energy consumption of these nodes. So, conservation of energy is the need of the hour. The main focus of the current study is to invise a routing mechanism that makes use of particle swarm optimization based on metaheuristic algorithm along with the relay node selection based on distances and residual energies. Experimental results show that the proposed protocol strikes a perfect balance between minimizing the number of relay nodes (to be positioned on subject) along with the energy efficient WBAN.

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“…It is expected that the scheme should be able to explore feasible regions of the NLP in the early stages, and exploit for the global optimum later on. The 40 search abilities of a range of EAs on the NLP (including genetic algorithms [22][64], evolution strategies [27], evolutionary programming [4], differential evolution [14], particle swarm optimisation [20,13], and many others) have been extensively studied.…”

mentioning

confidence: 99%

A multi-cycled sequential memetic computing approach for constrained optimisation

Sun

Garibaldi

Zhang

et al. 2016

Information Sciences

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Abdallah (2016) A multicycled sequential memetic computing approach for constrained optimisation. Information Sciences, 340-341 . pp. 175-190. ISSN 1872-6291 Access from the University of Nottingham repository: http://eprints.nottingham.ac.uk/35193/1/ICMA_FinalVersion.pdf Copyright and reuse:The Nottingham ePrints service makes this work by researchers of the University of Nottingham available open access under the following conditions. This article is made available under the Creative Commons Attribution Non-commercial No Derivatives licence and may be reused according to the conditions of the licence. For more details see: http://creativecommons.org/licenses/by-nc-nd/2.5/ A note on versions:The version presented here may differ from the published version or from the version of record. If you wish to cite this item you are advised to consult the publisher's version. Please see the repository url above for details on accessing the published version and note that access may require a subscription. AbstractIn this paper, we propose a multi-cycled sequential memetic computing structure for constrained optimisation. The structure is composed of multiple evolutionary cycles.At each cycle, an evolutionary algorithm is considered as an operator, and connects with a local optimiser. This structure enables the learning of useful knowledge from previous cycles and the transfer of the knowledge to facilitate search in latter cycles.Specifically, we propose to apply an estimation of distribution algorithm (EDA) to explore the search space until convergence at each cycle. A local optimiser, called DONLP2, is then applied to improve the best solution found by the EDA. New cycle starts after the local improvement if the computation budget has not been exceeded.In the developed EDA, an adaptive fully-factorized multivariate probability model is proposed. A learning mechanism, implemented as the guided mutation operator, is adopted to learn useful knowledge from previous cycles.The developed algorithm was experimentally studied on the benchmark problems in the CEC 2006 and 2010 competition. Experimental studies have shown that the developed probability model exhibits excellent exploration capability and the learning mechanism can significantly improve the search efficiency under certain conditions.The comparison against some well-known algorithms showed the superiority of the de- * Corresponding author Email address: j.sun@greenwich.ac.uk (Jianyong Sun)Preprint submitted to Information Sciences July 19, 2016 veloped algorithm in terms of the consumed fitness evaluations and the solution quality.

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Advanced particle swarm assisted genetic algorithm for constrained optimization problems

Cited by 55 publications

References 33 publications

Optimization of an experimental unsymmetrical-unbalanced two-phase induction motor using the HGAPSO hybrid technique and the finite element method for increasing efficiency and reducing torque ripple

Optimization of an experimental unsymmetrical-unbalanced two-phase induction motor using the HGAPSO hybrid technique and the finite element method for increasing efficiency and reducing torque ripple

PSOBAN: a novel particle swarm optimization based protocol for wireless body area networks

A multi-cycled sequential memetic computing approach for constrained optimisation

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